Seminar Computational Neuroscience


Computational neuroscience uses quantitative methods to describe what nervous systems do, study how they function, and explain the underlying principles. This seminar will cover recent scientific publications in the field of computational neuroscience. Specific topics:

  • Neural Encoding
  • Neural Decoding
  • Information Theory
  • The Action Potential
  • Dynamics of Neural Networks
  • Synaptic Plasticity
  • Associative Networks
  • Continuous Attractor Networks
  • Associative Learning
  • Classification
  • Competitive Learning
  • Generative Models
Learning Outcomes:
After successful completion of this seminar, students will be able to
  • read and understand scientific articles in computational neuroscience
  • apply computational models to describe the functioning of the nervous system
  • understand the advantages and disadvantages of specific computational models
  • discuss how neuroscience experiments are used to test computational models
  • present the results of studies in computational neuroscience to an audience
Oral presentation



Course type
Summer Term 2023


Takes place every week on Wednesday from 10:00 to 12:00 in room NB 3/57.
First appointment is on 05.04.2023
Last appointment is on 12.07.2023


Knowledge of calculus, linear algebra, and statistics are required, e.g. Mathematik 1 und 2, Statistik. Knowledge of biology is not necessary, but basic computational neuroscience is.
Students should have taken the class „Introduction to Computational Neuroscience“, or something equivalent, before enrolling in this seminar. It is also possible to take this seminar in parallel with „Introduction to Computational Neuroscience“.


The articles will be announced in the first meeting.
Background reading: “Theoretical Neuroscience” by Dayan and Abbott, MIT Press

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210